Image Classification for watermarks with poor results
Well probably the response is that previous approach was a little naive.
I managed to fave some interesting result with this kernel that allow me to have an accuracy of 0.969  and a validation accuracy of 0.931.
Model I used is based on ResNet50 with the following additional layers ( and the last one just for binary classification ):
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(32, activation='relu'),
tf.keras.layers.Dense(1,activation=&quot;sigmoid&quot;)

Even if the net is already trained, I trained each layer again otherwise I did not have any sensible accuracy.
Training history is like this:

Still far to be really good, but progress.